{"id":680,"date":"2016-06-08T12:34:38","date_gmt":"2016-06-08T12:34:38","guid":{"rendered":"http:\/\/quantpedia.com\/?p=680"},"modified":"2025-06-04T14:09:50","modified_gmt":"2025-06-04T12:09:50","slug":"trend-model-via-difference-between-long-and-short-term-variance","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/trend-model-via-difference-between-long-and-short-term-variance\/","title":{"rendered":"Trend Model via Difference Between Long- and Short-Term Variance"},"content":{"rendered":"

\n\tRelated to CTA\/trendfollowing strategies:
\n\t
\n\tAutores: <\/strong>Bouchaud, Dao, Deremble, Lemperiere, Nguyen, Potters<\/p>\n

\n\tT\u00edtulo: <\/strong>Tail Protection for Long Investors: Convexity at Work<\/p>\n

\n\tLink:<\/strong> http:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2777657<\/a><\/p>\n

\n\tAbstracto:<\/strong>
\n\t
\n\tWe relate the performance of trend following strategy to the difference between a long-term and a short-term variance. We show that this result is rather general, and holds for various definitions of the trend. We use this result to explain the positive convexity property of CTA performance and show that it is a much stronger effect than initially thought. This result also enable us to highlight interesting connections with Risk Parity portfolio. <\/font>Finally, we propose a new portfolio of options that gives us a pure exposure to the variance of the underlying, shedding some light on the link between trend and volatility, and also helping us understanding the exact role of hedging.<\/font><\/p>\n

\n\tFragmentos destacados del art\u00edculo de investigaci\u00f3n acad\u00e9mica:<\/strong><\/p>\n

\n\t"In this paper, we have shown that a single-asset trend has a built-in convexity if we aggregate its returns over the right time-scale. This becomes apparent if we rewrite the performance of the trend as a swap between the variance defined over long-term returns (typically the time scale of the trending filter) and the one defined over short-term returns (the rebalancing of our portfolio). This feature appears to hold for various filters and saturation levels.<\/p>\n

\n\tThe importance of these 2 time-scales has been underlined, and it is clear that the convexity (and the hedging properties) are only present over long-term time scales (as defined by the trending filter itself): it is wrong to expect a 6-month trending system rebalanced every week to hedge against a market crash that lasted only a few days.<\/strong><\/p>\n

\n\tWe also turned our attention to CTA indices, and particularly the SG CTA Index. We have proposed a simple replication index, using a very natural un-saturated trend on a pool of very liquid assets. Assuming realistic fees, and fitting only the time-scale of the lter, we get a very good correlation (above 80%), and capture the drift completely. This shows again that CTAs are simply following a long-term trending signal, and there is little added value in their idiosyncrasies.<\/p>\n

\n\tHowever, this also shows us that a CTA does not provide the same hedge a single-asset trend provides: some of the convexity is lost because of diversication. We however have found that CTAs do offer an interesting hedge to Risk-Parity products, which we approximated with a very good precision by long positions on the main asset classes.A ll in all, these results prove that a trending system does offer protection to long-term large moves of the market.<\/p>\n

\n\tWe then turned our attention to the link between trend and volatility. We found that a simple trending toy-model shares an exposure to the long-term variance with a naked straddle. The difference is the fact that the entry price for the straddle is fixed by the at-the-money volatility, while the trend pays the realized short-term variance. We then propose a very clean way to get exposure to this short term variance by using the trending toy-model as a hedging strategy for a portfolio of strangles. This is a simple, model-free portfolio that offers the same pay-off than traditional variance swaps."<\/p>\n\n

\n\n\n\n

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\n\tRelated to CTA\/trendfollowing strategies:<\/p>\n

\tAutores: <\/strong>Bouchaud, Dao, Deremble, Lemperiere, Nguyen, Potters<\/p>\n

\n\tT\u00edtulo: <\/strong>Tail Protection for Long Investors: Convexity at Work<\/p>\n

\n\tLink:<\/strong> http:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2777657<\/a><\/p>\n

\n\tAbstracto:<\/strong><\/p>\n

\tWe relate the performance of trend following strategy to the difference between a long-term and a short-term variance. We show that this result is rather general, and holds for various definitions of the trend. We use this result to explain the positive convexity property of CTA performance and show that it is a much stronger effect than initially thought. This result also enable us to highlight interesting connections with Risk Parity portfolio. <\/font>Finally, we propose a new portfolio of options that gives us a pure exposure to the variance of the underlying, shedding some light on the link between trend and volatility, and also helping us understanding the exact role of hedging.<\/font><\/p>\n

\n\tFragmentos destacados del art\u00edculo de investigaci\u00f3n acad\u00e9mica:<\/strong><\/p>\n

\n\t"In this paper, we have shown that a single-asset trend has a built-in convexity if we aggregate its returns over the right time-scale. This becomes apparent if we rewrite the performance of the trend as a swap between the variance defined over long-term returns (typically the time scale of the trending filter) and the one defined over short-term returns (the rebalancing of our portfolio). This feature appears to hold for various filters and saturation levels.<\/p>\n

\n\tThe importance of these 2 time-scales has been underlined, and it is clear that the convexity (and the hedging properties) are only present over long-term time scales (as defined by the trending filter itself): it is wrong to expect a 6-month trending system rebalanced every week to hedge against a market crash that lasted only a few days.<\/strong><\/p>\n

\n\tWe also turned our attention to CTA indices, and particularly the SG CTA Index. We have proposed a simple replication index, using a very natural un-saturated trend on a pool of very liquid assets. Assuming realistic fees, and fitting only the time-scale of the lter, we get a very good correlation (above 80%), and capture the drift completely. This shows again that CTAs are simply following a long-term trending signal, and there is little added value in their idiosyncrasies.<\/p>\n

\n\tHowever, this also shows us that a CTA does not provide the same hedge a single-asset trend provides: some of the convexity is lost because of diversication. We however have found that CTAs do offer an interesting hedge to Risk-Parity products, which we approximated with a very good precision by long positions on the main asset classes.A ll in all, these results prove that a trending system does offer protection to long-term large moves of the market.<\/p>\n

\n\tWe then turned our attention to the link between trend and volatility. We found that a simple trending toy-model shares an exposure to the long-term variance with a naked straddle. The difference is the fact that the entry price for the straddle is fixed by the at-the-money volatility, while the trend pays the realized short-term variance. We then propose a very clean way to get exposure to this short term variance by using the trending toy-model as a hedging strategy for a portfolio of strangles. This is a simple, model-free portfolio that offers the same pay-off than traditional variance swaps."<\/p>\n

\n

\n\tAre you looking for more strategies to read about? Check http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Screener<\/a><\/strong><\/p>\n

\n\tDo you want to see performance of trading systems we described? Check<\/strong> http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Chart\/Performance<\/a><\/strong><\/p>\n

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